263 results about "Generative Design" patented technology

The invention provides a generative adversarial-driven intelligent security defense method and system, and solves the problem of dynamic network security defense through three-layer architecture innovation: 1, data fusion layer reconstruction: employing a multi-modal feature extraction engine driven by an MoE architecture, dynamically allocating computing power resources to a plurality of expert models, and improving the heterogeneous data distillation efficiency; an LLM for fine adjustment in the security field is introduced, a cross-modal semantic similarity matrix is constructed, and the accuracy of unstructured threat intelligence analysis is improved; a second dynamic attack and defense layer is constructed, a GPT-4 architecture attack generator is deployed, and generation of a multi-stage APT attack chain is simulated; a double-agent reinforcement learning framework is designed, and the confrontation training efficiency is improved; upgrading a three-cognitive decision-making layer, constructing a dynamic threat map based on a time sequence diagram neural network, and updating an adjacent matrix in real time; a plurality of agent clusters are deployed, the capabilities of encrypted traffic analysis and attack blocking are improved, and the problems of data layer defects, attack and defense confrontation limitation and decision-making layer bottleneck in the prior art are solved.

The invention discloses a personalized design intelligent interaction method based on topological optimization, and the method comprises the steps: S1, constructing a user behavior monitoring system based on multi-modal data collection, and generating a high-dimensional data set containing time sequence features; s2, based on an adaptive deep neural network model, performing dynamic prediction and hierarchical modeling on the personalized demand of the user; s3, generating an optimal solution of the design parameters in real time through a dynamic topology reconstruction algorithm; s4, iteratively updating a design scheme generation rule through a reinforcement learning mechanism; s5, constructing a multi-objective design space exploration and optimization framework supported by the neural network, and rapidly screening and optimizing design parameters; s6, dynamic updating and local optimization of the design scheme are achieved through multi-dimensional data mapping and parameterization regulation and control means; and S7, establishing a data-driven continuous learning and evolution mechanism, continuously optimizing the user demand prediction model and designing an optimization algorithm. The method has the advantages of being high in dynamic adaptability, high in intelligent level and high in personalized meeting precision.

The invention provides a BIM technology and project amount intelligent verification method and system, and the method comprises the steps: carrying out the word segmentation and part-of-speech tagging of a progress log, extracting an excavation region, a completion amount and a timestamp of the day, and obtaining a construction progress information set; according to the construction progress information set, identifying a semantic relationship of an excavation area, a completion volume and a time triple, and determining time-space semantic representation; extracting geometric information of each target earthwork logic body from the set of earthwork logic bodies, and calculating a design engineering quantity of each associated logic body in a corresponding preset elevation range in real time in combination with corresponding elevation data extracted from the BIM model to generate a design quantity data set; and according to the early warning result, updating the state attribute of the earthwork logic body in the BIM model, generating a dynamically updated BIM model state in combination with the deviation occurrence timestamp and the difference value, extracting the spatial distribution of the earthwork logic body in the BIM model, and generating a construction path.

The invention provides an interactive system virtual test method and device based on multi-mode cognitive twin bodies. The method comprises the following steps: 1) constructing user cognitive twin: outputting a behavior decision tree with confidence through an adversarial generative network based on population attributes, cognitive preferences and behavior history; 2) creating a dynamic interaction environment: encoding a to-be-tested interface into a semantic graph structure which can be analyzed by a machine, and associating a physical operation constraint rule base; 3) running a multi-agent test: deploying a twin cluster in the virtual sandbox, and recording an operation path, an attention thermodynamic diagram and decision delay; and 4) generating a design defect map, namely, positioning an interface cognitive friction point by comparing the deviation degree of an expected interaction flow and an actual behavior track. According to the method, the problems that a traditional user test period is long and scene coverage is limited are solved, and early interception of interaction risks and design optimization preposition are achieved. The method can be widely applied to high-risk / high-cost / high-cycle test scenes of the man-machine interaction system.

Example embodiments may include: obtaining a design specification of a graphical user interface (GUI), wherein the design specification is compatible with a GUI design tool and includes a layout of design artifacts; generating, through use of a component mapper, respectively corresponding GUI components for the design artifacts in accordance with the layout; populating, based on properties of the design artifacts, respectively corresponding properties of the GUI components; and generating, based on the layout, the GUI components, and their corresponding properties, a deployable software implementation of the GUI.

The invention relates to the technical field of gift packaging three-dimensional modeling, and discloses a construction method of a gift box packaging design three-dimensional model. The method comprises the following steps: collecting natural language description, a freehand sketch image and a physical material attribute parameter; analyzing a natural language to construct a design intention map, and analyzing a sketch to generate a geometric feature code; performing knowledge alignment fusion on the two to form enhanced design semantic representation; generating an initial three-dimensional design framework by using a generative design algorithm; material parameters are integrated, compatibility simulation test is carried out, parameter self-adaptive adjustment circulation is started according to a feasibility evaluation report, and component parameters are dynamically optimized; and rendering and outputting a final model. Through deep fusion of multi-modal design information and preposed dynamic optimization of physical attributes, the accuracy of design intention expression and the producibility of a three-dimensional model are improved, and automation and intelligentization of a design process are realized.

The invention belongs to the related technical field of composite material additive manufacturing, and discloses a composite material microstructure reverse design method and system based on a generative model, and the method comprises the steps: constructing a sample data set corresponding to a microstructure image-attribute parameter of a composite material; performing conditional embedding enhancement on the unconditional generation model, constructing a conditional embedding generation model, training the conditional embedding generation model by using a sample data set corresponding to a microstructure image-attribute parameter, the attribute parameter being an embedding condition during image generation, and obtaining a trained model as a reverse design model; and generating a design microstructure image by using a reverse design model according to the expected attribute parameters. According to the method, a reverse design process of quickly and accurately generating a corresponding vivid microstructure according to expected attributes is realized on the basis of powerful image generation and conditional embedding capability of the generative model, and the problems of long design period and uncontrollable effect are solved.

A phononic crystal generative design method based on a diffusion model and an unstructured polygonal mesh belongs to the field of phononic crystal design, and comprises the following steps: firstly, generating phononic crystal unit cells, calculating a dispersion curve of the phononic crystal unit cells, and constructing a data set; secondly, constructing a diffusion model and designing a neural network to learn a reverse process of the diffusion model; inputting the training data into the neural network for training until a termination condition is met; and finally, extracting a dispersion curve from the test data set as a reverse design target, quickly generating a group of photonic crystals by using a neural network and a diffusion model sampling algorithm, calculating the dispersion curve corresponding to the generated photonic crystals, and calculating an error between the dispersion curve and the reverse design target. According to the method, the dependence of an existing generative design method on a rule design domain can be broken through, and the adaptability to irregular geometrical shapes and complex boundaries is improved; efficient learning of the complex mapping relation between the mechanical attributes and the geometric configuration is achieved; the method has the advantages of high design speed and high precision.

Systems and processes enhance cybersecurity by dynamically generating a decoy sub-application that operates parallel to the primary application using generative design and a large language model. The core feature involves real-time anomaly detection within application traffic, utilizing AI to assess threats and orchestrate appropriate responses. Upon identifying potential security threats, the system employs generative design principles to architect a restricted-functionality sub-application, deploying it instantly to engage and analyze the attack vectors without compromising sensitive production data. The sub-application is isolated through software-defined networking, ensuring that its operations do not affect the primary application's functionality. Additionally, sophisticated traffic redirection mechanisms are employed to divert suspicious traffic from the primary to the decoy application, thereby protecting the integrity while allowing detailed threat analysis. This dual-capability system not only safeguards against disruptions but also enhances adaptive security measures through continuous learning and system adjustments.

The invention discloses a digital research and development demand automatic disassembling method based on knowledge reasoning. The method comprises the following steps: gathering multi-source demands, uniformly de-duplicating, and establishing a version baseline; a semantic graph is generated according to the standardized requirements, a support graph and a constraint set are constructed, and a resource portrait, historical measurement and a template library are formed; performing retrieval and sub-graph aggregation by using an improved GraphRAG model, completing alignment reasoning and outputting a fact set, a conflict list and a deletion list; the method comprises the following steps: generating a hierarchical task, dependency and priority by using an improved Reflexion algorithm, and performing shadow scheduling and acyclic verification to obtain an execution sequence table; generating a design / interface / test / verification / acceptance workpiece, and solving resources, milestones and key paths according to the design / interface / test / verification / acceptance workpiece; and a tracing link and an early warning mechanism are established, and rearrangement and resource redistribution are carried out when the time exceeds the limit. According to the method, the schedule forming time is shortened, the reworking and delay risks are reduced, and the workpiece coverage rate, the critical path controllability and the delivery traceability are improved.

Methods, systems, and apparatus, including medium-encoded computer program products, for computer aided design of physical structures include: obtaining a design space, design criteria, and boundary conditions for numerical simulation; defining an application of the boundary conditions to a voxelized mesh, including specifying a distribution of a load to nodes of voxels in the voxelized mesh that correspond to a surface of preserve geometry; and iteratively modifying a generatively designed three dimensional shape in the design space in accordance with the design criteria and a physical response of the modeled object determined by the numerical simulation performed using the application of the boundary conditions to the voxelized mesh, wherein the distribution of the total loading value during determination of the physical response ensures an equivalence between the total loading value and a sum of loading values distributed to the respective nodes of the voxels that correspond to the surface.

A generative machine learning model, such as a convolutional neural network (CNN), can be trained with solutions from a topology optimization solver for a solution for a topology of a set of structures so that the generative machine learning model can generate a plurality of alternative designs for a structure that are alternative topology optimizations (for the structure) for a set of initial setup parameters. The generative model when being trained includes a generative network and a discriminator network. The generative model can be trained using outputs from a CNN autoencoder for densities and a CNN autoencoder for strain energies.

The invention discloses a 3D printing self-adaptive wing design method based on generative artificial intelligence, and relates to the crossing field of 3D printing and artificial intelligence. In order to solve the technical problems of complex manufacturing, low reliability and large weight of a traditional process in the prior art, the technical scheme provided by the invention comprises the following steps: pre-training a large language model to obtain a trained basic model; receiving self-adaptive wing structure design parameters input by a user and generating a design scheme script; calling three-dimensional modeling software, automatically establishing a wing three-dimensional model based on the design scheme script, and outputting a model file; calling simulation analysis software, performing wing performance simulation analysis based on the model file, and outputting analysis result data; and calling the basic model to carry out design scheme evaluation and iterative optimization based on the analysis result data until an optimized model file meeting the design requirement is generated. The method can be applied to intelligent design and automatic manufacturing of self-adaptive wing structures of aerospace crafts.

The invention discloses a multi-objective optimization method for the form of an urban block, and the method comprises the following steps: S100, constructing a parameterized model, carrying out the experimental design, defining a group of geometric parameters used for describing the form of the urban block, and generating a plurality of sample points in the value range of the geometric parameters through employing a sampling method; s200, bidirectional coupling numerical simulation is executed, a data set is generated, at least one thermal comfort evaluation index and one air quality evaluation index are output, and therefore the data set containing input parameters and output indexes is formed; step S300, constructing and verifying a machine learning agent model to obtain an agent model capable of rapidly predicting the evaluation index; step S400, executing multi-objective optimization search, and automatically searching to enable the thermal comfort evaluation index and the air quality evaluation index to simultaneously achieve a better Pareto optimal solution set; and step S500, analyzing and outputting an optimization result, extracting an optimization value range of the geometric parameters, and generating a design guide.

Described herein is a computer implemented method for generating a plurality of synthesised designs based on a selected design that includes a first element. The method includes accessing design generation data that includes source data and field association data. The source data defines a first field and a first design generation record that defines a first content item that is an image-type content item and that is associated with the first field. The field association data defines a first association between the first field and the first element. The method further includes generating a first synthesised design that is based on the selected design and that is generated such that the first element of the selected design is updated in the first synthesised design using the first content item.

The invention discloses a traditional building micro-reconstruction performance improving system based on machine learning. According to the method, the inherent contradiction between traditional building protection and performance improvement is effectively broken through an intelligent multi-dimensional data fusion and generative design technology. A holographic information model constructed by the multi-source data acquisition module completely retains historical genes and space passwords of a building, and provides an accurate space-time reference for subsequent transformation; a two-channel analysis mechanism of the performance evaluation module brings physical structure safety and cultural value continuity into a unified evaluation system, and ensures that a transformation scheme not only meets modern use requirements but also inherits and builds wisdom. The pattern generation and space reconstruction technology of the module is creatively designed, the limitation of traditional craftsman experience inheritance is broken through, contemporary expression of traditional building languages is achieved in the digital twinning environment, and historical style protection is changed from passive limitation to active innovation.

Variants of the method can include determining a design, generating a geometric representation of the design, generating a topological representation of the design, determining a context value set, predicting a physical field using a trained physics model, optionally verifying the physical field, optionally generating a corrected physical field, and optionally training the physics model based on the corrected physical field.

The invention relates to the technical field of electric power iron tower full-life-cycle carbon footprint accounting, provides an electric power iron tower full-life-cycle carbon footprint accounting method and system, and aims to solve the problems of low efficiency and poor accuracy of electric power iron tower full-life-cycle carbon footprint accounting in the prior art. The method comprises the steps that an electric power iron tower component library is constructed, and a design stage carbon accounting benchmark is generated in combination with a building information model, a steel model, a welding process and anti-corrosion coating parameters; capturing assembly installation deviation and splicing gaps in real time, calculating material loss caused by construction errors, and converting the material loss into carbon emission increment; loading data by using a digital twinning technology, adjusting a material loss early warning threshold value, and updating design data; based on the corrected data, icing load deformation and corrosion expansion in the operation and maintenance stage are analyzed, and a full-life-cycle carbon footprint report is generated in combination with the updated stress distribution data. According to the technical scheme provided by the invention, the efficiency and accuracy of the full-life-cycle carbon footprint accounting of the electric power iron tower can be improved.

The invention discloses a bridge substructure modeling scheme generation method and device, equipment and a medium, and relates to the technical field of bridge engineering, and the method comprises the steps: obtaining bridge superstructure design input parameters, constructing substructure design task information, and generating design parameters through big language model reasoning; and a modeling script is automatically generated, and structural response calculation is carried out. Different modeling strategies are judged and executed according to the calculation result, finally, a target modeling scheme meeting the requirement is intelligently generated, design parameter generation and automatic modeling of the bridge substructure in a non-standard bridge type scene are achieved, and the design efficiency and accuracy are improved.

The invention relates to the field of motor structure design and optimization, and discloses a cogging torque and torque ripple optimization method for a permanent magnet motor for a vehicle. The method comprises the steps that based on an electromagnetic scheme, a geometric boundary and a term dictionary, a parameterized geometric prototype containing four symmetric semi-arc auxiliary grooves is constructed, and a design variable set structure is generated; converting the structure into a target evaluation interface structure containing a target item and a constraint item; an improved adaptive particle swarm algorithm is combined with finite element rapid check and a new iteration function to carry out optimization solution, and an updated candidate group structure is generated; and finally, optimal parameters are screened through Pareto sorting and robustness recheck, and an engineering configuration structure is generated. According to the method, through systematic parameter modeling, an efficient collaborative optimization algorithm and simulation evaluation, the optimization efficiency and reliability are remarkably improved, the cogging torque and the torque ripple of the motor are effectively reduced, and meanwhile, the process feasibility and the performance robustness of a design scheme are ensured.

The invention relates to a product customization and configuration method and system based on a product model and a knowledge base. The method comprises the following steps: (a) associating a product structure model and a product knowledge base of a target product series; (b) constructing a product configuration model integrating a product structure model, a product knowledge graph and a configuration rule; (c) customer demand parameters are received, and a customized configuration scheme is generated through the product configuration model; (d) automatically generating a design material list and a process material list based on the customized configuration scheme; and (e) executing cost accounting according to the generated design bill of material and the process bill of material and outputting quotation data. The method has the advantages that the requirement of a user for customization can be met, the requirement for configuration can also be met, data such as technologies and processes can be automatically configured for the user, the data can be synchronously updated to related software systems such as PLM and PDM, and the intelligent design and intelligent manufacturing capacity of an enterprise is greatly improved.

The invention relates to the technical field of automobile design, in particular to an intelligent generation type design method of an anti-collision beam, which comprises the following steps: firstly, performing experimental analysis on the anti-collision beam to obtain a section image comprising the anti-collision beam and performance response data corresponding to the section image; performing image recognition and text extraction on the section image; fusing the recognized image and the extracted text by using a multi-modal multi-layer fusion model to obtain a multi-modal design variable; training the constructed conditional variation network by using the multi-modal design variables and the performance response data corresponding to the multi-modal design variables to obtain a conditional variation model; generating design variables by using the conditional variation model, and predicting performance response data corresponding to the variables; optimal performance response data are screened out from the obtained performance response data, and then the optimal design scheme of the anti-collision beam to be designed is obtained. According to the method, the optimal design scheme is determined by using the conditional variation network, and the efficiency and the precision of optimization design are effectively improved.

The invention discloses a jewelry 3D model design method and system. The method comprises the steps of obtaining historical jewelry design full-modal data; carrying out multi-dimensional feature extraction and association labeling on the full-modal data by utilizing computer vision and natural language processing technologies so as to construct a three-dimensional feature matrix; according to the three-dimensional feature matrix, constructing a cross-modal association database through cross-modal structured labeling; generating a multi-modal aesthetic feature database through a multi-task deep learning model by using the cross-modal association database; training a multi-task AI aesthetics evaluation model fusing geometry, styles and user preferences by using the multi-modal aesthetics feature database; and obtaining jewelry 3D design data through the graphical interface, and performing quantitative evaluation of 3D design by using the multi-task AI evaluation model to generate a design evaluation result. Through the design method provided by the invention, the objectivity of jewelry 3D model design evaluation can be improved, so that the design efficiency and the design quality are improved.

The invention discloses a Solar-Think photovoltaic power station intelligent construction platform, and the platform comprises a photovoltaic power station intelligent design module which is used for automatically generating design data; the photovoltaic power station construction collaboration module is used for converting a design model in the design data into a digital twin model, collecting construction data in real time through Internet of Things equipment, and dynamically updating a construction state in a virtual space; the predictive operation and maintenance module is used for predicting equipment faults through an LSTM neural network based on design parameters, construction records and real-time operation and maintenance data of the design model and generating an optimal maintenance path in combination with the digital twinborn model; and the photovoltaic power station full-process data closed-loop architecture module is used for automatically synchronizing the design model to the construction collaboration module, reversely correcting the design model based on construction data, creating association between the construction data and the operation and maintenance data, forming an equipment full-life-cycle file, and transmitting the equipment full-life-cycle file to the construction collaboration module. And feeding back the operation and maintenance data to the design module to support next-generation power station scheme optimization.

The invention provides an automatic generation method supporting an MBSE system model and a design document. MBSE system model project information is read, and design document basic information is automatically generated; system design flow information of a methodological model constructed in an MBSE system model project is read, corresponding document chapters are generated according to flow design content C, the sequence of the document chapters is determined according to the design flow sequence A, and the outline level of each chapter in the design document is determined according to the hierarchical relation in the design flow hierarchy B; on the basis of the generated design document structure and the read design process corresponding model D, automatically filling each chapter with model content; and dynamically updating the content of the design document. The problems that in the prior art, a design document generation method based on templates and rules is poor in flexibility, and the document and the model are difficult to dynamically synchronize can be solved, and the consistency of the system model and the design document is guaranteed.

The invention belongs to the technical field of open source software supply chain security, and particularly relates to a security patch variant positioning method based on two-dimensional feature design. By designing the two-dimensional features and applying the rule model, comprehensive, accurate and automatic positioning of the security patch variants in the specified open source software code warehouse is realized. The method comprises the following specific steps: data preprocessing: extracting basic information in a submission record and carrying out normalization processing; feature generation: designing and calculating similar features and characterization features of submission record pairs; and variant prediction: predicting the extracted features by applying a random forest model, and judging whether the submitted record is the security patch variant according to a prediction score. According to the method, by widely mining similar features and characterization features between patch variants and combining with a rule-based model, the comprehensiveness, accuracy and automation degree of patch variant positioning are improved, and an effective patch variant positioning tool is provided for software security analysts.

The invention provides a reverse generation type design method and system of a battery material based on a large language model. The method comprises the following steps: carrying out serialized representation on a crystal structure of a lithium ion battery material; constructing a material basic model, and training the material basic model based on the serialized representation result to obtain a trained material basic model; a multi-dimensional attribute virtual KV attention mechanism is introduced into the material basic model, and a property conditional generation model is obtained; accessing a property prediction head network to the material basic model, and performing supervised training on the material basic model to obtain a material property prediction model; and performing reinforcement learning training on the property conditional generation model to obtain an optimized property conditional generation model, and generating a crystal structure candidate set based on the optimized property conditional generation model. By means of the method, efficient, controllable and diversified generation of the hundred-atomic-scale complex crystal structure is achieved, and the precision and practicability of material reverse design are remarkably improved.

The invention discloses a building analysis system based on a shape grammar theory, relates to the technical field of building analysis, and has the technical key points that the shape grammar theory is taken as a grammar, various generative design technical methods and algorithm language programs are integrated, and thinking and logic levels of building design are optimized; on the design process and design logic, operation objects are abstracted into definitions of basic shapes and rules, so that the design process is organized, clear, simple and easy to understand, the design efficiency and scheme quality when designers face related complex and large-amount problems are improved, the problem that design is stiff in rural dwellings design is solved, and the design efficiency is improved. And a new technical method is provided for traditional village building design.

The invention relates to the technical field of offshore wind power equipment, and discloses an offshore floating type wind turbine mooring system optimization method based on a proxy model, and the method comprises the following steps: S1, determining key design parameters; s2, sampling to generate design points; s3, establishing a total index function of multi-target coupling; s4, generating a sample database through numerical simulation; s5, constructing an agent model by using a Kriging method; s6, calling the proxy model to search an optimal solution by adopting a differential evolution algorithm; s7, performing numerical simulation verification on the optimal solution and judging convergence; and S8, if not, updating the database and returning to S5 for iteration, and if so, outputting the optimal configuration. According to the method, the problems that a traditional optimization method is high in simulation calculation cost and too long in consumed time are solved, searching is carried out through the proxy model instead of high-precision simulation, the verification iteration step is combined, the optimization efficiency is greatly improved while the result accuracy is guaranteed, and the multi-target mooring scheme with the optimal comprehensive performance can be efficiently obtained.